Machine Learning-Based Badminton Pace Training Teaching
DOI: 10.23977/autml.2022.030208 | Downloads: 19 | Views: 651
Author(s)
Cheng Zhou 1
Affiliation(s)
1 School of Physical Education, Hunan University of Arts and Science, Changde, Hunan, 415000, China
Corresponding Author
Cheng ZhouABSTRACT
In teaching practice and daily training, it is found that most students pay attention to hand skills in badminton training, but do not pay attention to the training of footwork, the movement technique is not standard, and the footwork movement is not flexible. These problems can hinder the improvement of motor skills and greatly increase the risk of sports injuries. In order to solve the shortcomings of the existing badminton pace training teaching research, this paper discusses the functional equation of the machine learning SVM classification algorithm and the types of badminton pace training teaching methods, aiming at the test indicators of the badminton pace training teaching application based on machine learning. And the test environment is briefly introduced. And the design and discussion of the teaching process structure of badminton pace training based on machine learning SVM classification algorithm, and finally the average recognition rate of four badminton paces in single training, mean training and weighted training by the machine learning SVM classification algorithm designed in this paper. Experimental test, experimental data show that the average recognition rate of forehand net pick, backhand net pick, back step overhead shot and back step forehand hit high ball in a single training based on machine learning SVM classification algorithm reached 0.895, 0.871, 0.789 and 0.920, the recognition rates in mean training and weighted training are in the range of 0.91 to 0.97, so it is verified that the model designed in this paper has better classification and recognition effects in badminton pace training teaching.
KEYWORDS
Machine Learning, SVM Classification Algorithm, Badminton Pace, Training TeachingCITE THIS PAPER
Cheng Zhou, Machine Learning-Based Badminton Pace Training Teaching. Automation and Machine Learning (2022) Vol. 3: 48-54. DOI: http://dx.doi.org/10.23977/autml.2022.030208.
REFERENCES
[1] Pounra J, Jaskar D R, Ranjith R M, et al. Consequence of Jump Rope Training and Kettle Bell Training on Selected Agility and Muscular Strength of College Men Badminton Players[J]. Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, 2020, 14(5):664-669.
[2] Wismanadi H, Kafrawi F R, Pramono M, et al. Rasio Interval Training Dalam Latihan Shadow Bulutangkis Terhadap Power dan Kecepatan[J]. Journal Sport AREA, 2020, 5(2):186-198.
[3] Alikhani R, Shahrjerdi S, Golpaigany M, et al. The effect of a six-week plyometric training on dynamic balance and knee proprioception in female badminton players [J]. JCCA. Journal of the Canadian Chiropractic Association. Journal de l'Association chiropratique canadienne, 2019, 63(3):144-153.
[4] A Bravo-Sánchez, J Abián-Vicén, AT Montalbán, et al. Acute effects of badminton practice on the surface temperature of lower limbs introduction [J]. Archivos de Medicina del Deporte, 2018, 35(4):239-244.
[5] JA Pérez-Turpin, Elvira C, Cabello-Manrique D, et al. Section III -Sports Training Notational Comparison Analysis of Outdoor Badminton Men's Single and Double Matches [J]. Journal of Human Kinetics, 2020, 71(2020):267-273.
[6] Nirendan J. Effect of shadow training on motor fitness components of badminton players [J]. International Journal of Physical Education, Fitness and Sports, 2019, 1(2):04-06.
[7] Nugroho S, Nasrulloh A, Karyono T H, et al. Effect of intensity and interval levels of trapping circuit training on the physical condition of badminton players[J]. Journal of Physical Education and Sport, 2021, 21(3):1981-1987.
[8] Pounraj, Jaskar D R, Ranjith R M, et al. Consequence of Jump Rope Training and Kettle Bell Training on Selected Agility and Muscular Strength of College Men Badminton Players[J]. Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, 2020, 14(5):664-669.
[9] Baydin A G, Pearlmutter B A, Radul A A, et al. Automatic differentiation in machine learning: A survey [J]. Journal of Machine Learning Research, 2018, 18(153):1-43.
[10] Butler K T, Davies D W, Hugh C, et al. Machine learning for molecular and materials science[J]. Nature, 2018, 559(7715):547-555.
[11] Malta T M, Sokolov A, Gentles A J, et al. Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation [J]. Cell, 2018, 173(2):338-354.
Downloads: | 1745 |
---|---|
Visits: | 71314 |
Sponsors, Associates, and Links
-
Power Systems Computation
-
Internet of Things (IoT) and Engineering Applications
-
Computing, Performance and Communication Systems
-
Journal of Artificial Intelligence Practice
-
Advances in Computer, Signals and Systems
-
Journal of Network Computing and Applications
-
Journal of Web Systems and Applications
-
Journal of Electrotechnology, Electrical Engineering and Management
-
Journal of Wireless Sensors and Sensor Networks
-
Journal of Image Processing Theory and Applications
-
Mobile Computing and Networking
-
Vehicle Power and Propulsion
-
Frontiers in Computer Vision and Pattern Recognition
-
Knowledge Discovery and Data Mining Letters
-
Big Data Analysis and Cloud Computing
-
Electrical Insulation and Dielectrics
-
Crypto and Information Security
-
Journal of Neural Information Processing
-
Collaborative and Social Computing
-
International Journal of Network and Communication Technology
-
File and Storage Technologies
-
Frontiers in Genetic and Evolutionary Computation
-
Optical Network Design and Modeling
-
Journal of Virtual Reality and Artificial Intelligence
-
Natural Language Processing and Speech Recognition
-
Journal of High-Voltage
-
Programming Languages and Operating Systems
-
Visual Communications and Image Processing
-
Journal of Systems Analysis and Integration
-
Knowledge Representation and Automated Reasoning
-
Review of Information Display Techniques
-
Data and Knowledge Engineering
-
Journal of Database Systems
-
Journal of Cluster and Grid Computing
-
Cloud and Service-Oriented Computing
-
Journal of Networking, Architecture and Storage
-
Journal of Software Engineering and Metrics
-
Visualization Techniques
-
Journal of Parallel and Distributed Processing
-
Journal of Modeling, Analysis and Simulation
-
Journal of Privacy, Trust and Security
-
Journal of Cognitive Informatics and Cognitive Computing
-
Lecture Notes on Wireless Networks and Communications
-
International Journal of Computer and Communications Security
-
Journal of Multimedia Techniques
-
Computational Linguistics Letters
-
Journal of Computer Architecture and Design
-
Journal of Ubiquitous and Future Networks